Beyond Numbers: A swift EDA of Saudi Arabia's Population dataset

About Author

• Project: Population of Saudi Arabia
• Author: Syed Salman Bacha
• Code Submission Date: 25-11-2023
• Author's Contact Info:\

Email
Github
kaggle
Linkedin
twitter

About Data

• Data: Apple AppStore
• Data Age: The data was collected in the month of October 2021.
• Dataset: 🔗 link

---

Description: Explore the diverse demographics of Saudi Arabia through this comprehensive dataset showcasing population statistics across various parameters. The dataset contains records detailing the population dynamics in terms of gender, nationality, region, and year.

Columns:

• Gender: Specifies the gender of individuals (e.g., male, female).
• Nationality: Represents the nationality of individuals residing in Saudi Arabia.
• Region: Indicates the geographical region within Saudi Arabia.
• Year: Represents the year of the recorded population data.
• Population: Denotes the population count based on the respective parameters.

Key Insights:

1. Temporal Population Trends: Unveil the changing population dynamics over the years within Saudi Arabia.
2. Gender Distribution: Explore the distribution and changes in population based on gender across different regions and years.
3. Nationality Diversity: Investigate the demographic diversity concerning various nationalities residing in Saudi Arabia.
4. Regional Variations: Analyze how population sizes differ across the diverse regions within the country.

Why Explore this Dataset?

• Cultural Diversity: Gain insights into the diverse cultural landscape of Saudi Arabia through population demographics.
• Socio-Economic Analysis: Understand how population dynamics impact socio-economic aspects within different regions.
• Policy Implications: Potentially inform policies based on trends observed in population demographics over time.

---

Loading the important libraries

import pandas as pd
import numpy as np 
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

Loading the data

df = pd.read_csv('KSA_population.csv')

Lets take a look at the data

df.head()

	Gender	Nationality	Region	Year	Population
0	Female	Non Saudi	Al Bahah	2010	16209
1	Female	Non Saudi	Al Bahah	2011	16521
2	Female	Non Saudi	Al Bahah	2012	16752
3	Female	Non Saudi	Al Bahah	2013	17508
4	Female	Non Saudi	Al Bahah	2014	17682

Shape of the data

# Step 1: Data shape
print(df.shape)
rows, columns = df.shape
print(f"Num of Rows: {rows} ") # instances
print(f"Num of Columns: {columns} ") # series
print(f"The size (rows x columns) is: {df.size}") # size
print(f"The Dimensions are: {df.ndim}") # dimensions

(676, 5)
Num of Rows: 676 
Num of Columns: 5 
The size (rows x columns) is: 3380
The Dimensions are: 2

Columns In the Data

df.columns

Index(['Gender', 'Nationality', 'Region', 'Year', 'Population'], dtype='object')

Number of Unique values in each Column

df.nunique()

Gender           2
Nationality      2
Region          13
Year            13
Population     675
dtype: int64

View summary of the dataset

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 676 entries, 0 to 675
Data columns (total 5 columns):
 #   Column       Non-Null Count  Dtype 
---  ------       --------------  ----- 
 0   Gender       676 non-null    object
 1   Nationality  676 non-null    object
 2   Region       676 non-null    object
 3   Year         676 non-null    int64 
 4   Population   676 non-null    int64 
dtypes: int64(2), object(3)
memory usage: 26.5+ KB

Important points:

• We can see that the dataset contains mixture of categorical and numerical variables.

• Categorical variables have data type object.

• Numerical variables have data type int64.

Statistical Summary of the Data

df.describe()

	Year	Population
count	676.000000	6.760000e+02
mean	2016.000000	5.587169e+05
std	3.744428	7.062714e+05
min	2010.000000	1.430400e+04
25%	2013.000000	1.098975e+05
50%	2016.000000	2.495590e+05
75%	2019.000000	6.180745e+05
max	2022.000000	3.406281e+06

Important points to note

• The above command df.describe() helps us to view the statistical properties of numerical variables. It excludes character variables.

• If we want to view the statistical properties of character variables, we should run the following command -

   df.describe(include=['object'])
  
  

• If we want to view the statistical properties of all the variables, we should run the following command -

  df.describe(include='all')
  

Univerate Analysis

1. Explore Gender Variable

df['Gender'].unique()

array(['Female', 'Male'], dtype=object)

We can see that the number of unique values in Gender variable is 2.

The two unique values are Male and Female

df['Gender'].value_counts()

Female    338
Male      338
Name: Gender, dtype: int64

f, ax = plt.subplots(figsize=(4, 4))
ax = sns.countplot(x="Gender", data=df, palette="Set1")
plt.show()

2. Explore Nationallity variable

df['Nationality'].nunique()

2

df['Nationality'].unique()

array(['Non Saudi', 'Saudi'], dtype=object)

We can see that the number of unique values in Nationality variable is 2.

The two unique values are Non Saudi and Saudi

df['Nationality'].value_counts()

Non Saudi    338
Saudi        338
Name: Nationality, dtype: int64

f, ax = plt.subplots(figsize=(8, 4))
ax = sns.countplot(y="Nationality", data=df, palette="Set1")
plt.show()

3. Explore Region Variable

df['Region'].nunique()

13

df['Region'].unique()

array(['Al Bahah', 'Al Jawf', 'Al Madinah Al Munawwarah', 'Al Qaseem',
       'Ar Riyadh', 'Aseer', 'Eastern Region', 'Hail', 'Jazan',
       'Makkah Al Mukarramah', 'Najran', 'Northern Borders', 'Tabuk'],
      dtype=object)

We can see that the number of unique values in Region variable are 13.

The 13 unique values are Al Bahah, Al Jawf, Al Madinah Al Munawwarah, Al Qaseem, Ar Riyadh, Aseer, Eastern Region, Hail, Jazan, Makkah Al Mukarramah, Najran, Northern Borders, Tabuk

f, ax = plt.subplots(figsize=(10, 4))
ax = sns.countplot(y="Region", data=df, palette="Set2")
plt.show()

4. Explore Year Variable

df['Year'].nunique()

13

Year is a numeric variable.

df['Year'].describe()

count     676.000000
mean     2016.000000
std         3.744428
min      2010.000000
25%      2013.000000
50%      2016.000000
75%      2019.000000
max      2022.000000
Name: Year, dtype: float64

#boxplot
sns.boxplot(data=df, y='Year')
plt.ylabel("Years")
plt.title("Box Plot of Your Year Variable")
plt.show()

#boxenplot
sns.boxenplot(data=df, y='Year')

<AxesSubplot:ylabel='Year'>

#violinplot
sns.violinplot(data=df, y='Year')
plt.figure(figsize=(6,8))

<Figure size 600x800 with 0 Axes>

<Figure size 600x800 with 0 Axes>

Important points

• The above three plots( Boxplot, boxenplot and Violinplot) shows us the distribution of a numeric column.
• It can help us to visualize outliers of a particular column

Explore Population Variable

df['Population'].describe()

count    6.760000e+02
mean     5.587169e+05
std      7.062714e+05
min      1.430400e+04
25%      1.098975e+05
50%      2.495590e+05
75%      6.180745e+05
max      3.406281e+06
Name: Population, dtype: float64

#Histogram
sns.histplot(data=df, x='Population', kde=True, bins= 5)
plt.xlabel("Population")
plt.ylabel("Frequency")
plt.title("Histogram of Population Variable")
plt.show()

#boxplot
sns.boxenplot(df, x= 'Population')

<AxesSubplot:xlabel='Population'>

#violinplot
sns.violinplot(df, x= 'Population')

<AxesSubplot:xlabel='Population'>

#boxplot
sns.boxplot(data=df, y='Population')
plt.ylim(top= 1000000)
plt.ylabel("Years")
plt.title("Box Plot of Your Year Variable")
plt.show()

# Checking skewness of the data
df['Population'].skew()

1.8921844651674402

• The skewness of the Population column is 1.89
• This means that the distribution of the population is slightly right-skewed.

Biavariate Analysis

Catagorical variables in the dataset

categorical = [var for var in df.columns if df[var].dtype=='O']

print('There are {} categorical variables\n'.format(len(categorical)))

print('The categorical variables are :', categorical)

There are 3 categorical variables

The categorical variables are : ['Gender', 'Nationality', 'Region']

# Lets take a look 
df[categorical].head()

	Gender	Nationality	Region
0	Female	Non Saudi	Al Bahah
1	Female	Non Saudi	Al Bahah
2	Female	Non Saudi	Al Bahah
3	Female	Non Saudi	Al Bahah
4	Female	Non Saudi	Al Bahah

Number of labels: Cardinality

The number of labels within a categorical variable is known as cardinality. A high number of labels within a variable is known as high cardinality. High cardinality may pose some serious problems in the machine learning model. So, I will check for high cardinality.

# check for cardinality in categorical variables

for var in categorical:
    
    print(var, ' contains ', len(df[var].unique()), ' labels')

Gender  contains  2  labels
Nationality  contains  2  labels
Region  contains  13  labels

Explore problems within categorical variables

1) Missing values in categorical variables

df[categorical].isnull().sum()

Gender         0
Nationality    0
Region         0
dtype: int64

2) Plotting the missing values

plt.rcParams['figure.figsize'] = (15,6)
sns.heatmap(df.isnull(),yticklabels = False, cbar = False , cmap = 'viridis')
plt.title("Missing null values")

Text(0.5, 1.0, 'Missing null values')

3) Data Distribution of the Variables

import seaborn as sns

# make sure that the variable 'categorical' is defined in a previous cell

sns.pairplot(df, hue= 'Gender')

<seaborn.axisgrid.PairGrid at 0x22c6b331490>

• Correlation in the dataset

correlation = df.corr()

plt.figure(figsize=(10,5))
plt.title('Correlation Heatmap of Rain in Australia Dataset')
ax = sns.heatmap(correlation, square=True, annot=True, fmt='.2f', linecolor='white')
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
ax.set_yticklabels(ax.get_yticklabels(), rotation=30)           
plt.show()

Import points:

• Population and Year are positively correlated

Questions

Q1. In which Year the population was the Highiest

df.groupby('Year')['Population'].sum().sort_values(ascending= False).plot(kind= 'bar')
plt.get_backend()

'module://matplotlib_inline.backend_inline'

• Creating new Column : Total_population_by_year

# Creating column('Total_population_by_year')
df['Total_population_by_year'] = df.groupby('Year')['Population'].transform('sum')
df.head()

	Gender	Nationality	Region	Year	Population	Total_population_by_year
0	Female	Non Saudi	Al Bahah	2010	16209	23978487
1	Female	Non Saudi	Al Bahah	2011	16521	25091867
2	Female	Non Saudi	Al Bahah	2012	16752	26168861
3	Female	Non Saudi	Al Bahah	2013	17508	27624004
4	Female	Non Saudi	Al Bahah	2014	17682	28309273

# Converting 'total_population_by_year' into millions 
df['Total_population_by_year']= round(df['Total_population_by_year'] / 1e6, 1)
df.head()

	Gender	Nationality	Region	Year	Population	Total_population_by_year
0	Female	Non Saudi	Al Bahah	2010	16209	24.0
1	Female	Non Saudi	Al Bahah	2011	16521	25.1
2	Female	Non Saudi	Al Bahah	2012	16752	26.2
3	Female	Non Saudi	Al Bahah	2013	17508	27.6
4	Female	Non Saudi	Al Bahah	2014	17682	28.3

# Renaming the column
df.rename(columns= {"Total_population_by_year": "Year_population(million)"}, inplace= True)
df.head()

	Gender	Nationality	Region	Year	Population	Year_population(million)
0	Female	Non Saudi	Al Bahah	2010	16209	24.0
1	Female	Non Saudi	Al Bahah	2011	16521	25.1
2	Female	Non Saudi	Al Bahah	2012	16752	26.2
3	Female	Non Saudi	Al Bahah	2013	17508	27.6
4	Female	Non Saudi	Al Bahah	2014	17682	28.3

Important points

• Division by 1e6: The expression df['Total_population_by_year'] / 1e6 divides the values in the 'Total_population_by_year' column by 1,000,000, effectively converting the population counts to millions.

• Rounding to One Decimal Place: The round() function is used to round the division result to one decimal place. This ensures the population values are presented with a reduced precision, providing a clearer view of the data in millions.

• Assignment to a New Column: The transformed values are then assigned back to the 'Total_population_by_year' column, replacing the original population counts with their corresponding values in millions, rounded to one decimal place.

Q2. What is the Popoulatipn of Male vs female in each Year

sns.barplot(data=df, x='Year', y='Population', hue='Gender',palette='Set1', errorbar= ('ci', 0))
plt.title("Population of Male/Female from 2010-2022")

Text(0.5, 1.0, 'Population of Male/Female from 2010-2022')

df.groupby(df['Gender']=="Male")["Population"].count()

Gender
False    338
True     338
Name: Population, dtype: int64

Question 3: What is the average population growth rate per year?

population_by_year = df.groupby('Year')['Population'].sum()
average_growth_rate = (population_by_year.iloc[-1] - population_by_year.iloc[0]) / (population_by_year.index[-1] - population_by_year.index[0])
print("Average population growth rate per year:", average_growth_rate)

Average population growth rate per year: 683061.4166666666

Question 4: Population Trends over Years

population_over_years = df.groupby('Year')['Population'].sum()

plt.figure(figsize=(10, 6))
population_over_years.plot(kind='line', marker='o')
plt.title('Population Trend over Years')
plt.xlabel('Year')
plt.ylabel('Population')
plt.grid(True)
plt.show()

Question 5: What is the Population Variation by Nationality over the years?

plt.figure(figsize=(10, 6))
sns.boxplot(data=df, x='Year', y='Population', hue='Nationality')
plt.title('Population Variation by Nationality Over Years')
plt.xlabel('Year')
plt.ylabel('Population')
plt.legend(title='Nationality', bbox_to_anchor=(1, 1))
plt.show()

Question 6: Which Region has the highiest population

px.bar( df.groupby('Region')['Population'].max().sort_values(ascending= False))

Question 7: What is the Male vs female ratio in the population

px.bar(df.groupby('Gender')['Population'].max().sort_values(ascending= False))

#plotting the data
px.bar(df, x= "Year", y= 'Population', color= 'Nationality')

# The code generates a bar plot using the specified DataFrame and columns, providing a visual representation of how population counts 
# in thousands vary over different years, segmented by nationality. 
# Each bar in the plot represents the population count for a specific nationality category in a given year.

• Population division in the region of Jazan

px.bar(df.groupby(df['Region']== 'Jazan')['Population'].mean())

fig = px.bar(df, x='Region', y='Population', title='Population by Region', 
             labels={'Population': 'Total Population', 'Region': 'Region'},
             color='Region')

fig.update_layout(barmode='stack')
fig.show()

Two libraries for Quick EDA

• I will use two Libraries for quick analysis of the dataset
• The two libraries are Skimpy and Dataprep

(1) Skimpy

import skimpy
from skimpy import skim
skim(df)

╭──────────────────────────────────────────────── skimpy summary ─────────────────────────────────────────────────╮
│          Data Summary                Data Types                                                                 │
│ ┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓ ┏━━━━━━━━━━━━━┳━━━━━━━┓                                                          │
│ ┃ dataframe         ┃ Values ┃ ┃ Column Type ┃ Count ┃                                                          │
│ ┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩ ┡━━━━━━━━━━━━━╇━━━━━━━┩                                                          │
│ │ Number of rows    │ 676    │ │ string      │ 3     │                                                          │
│ │ Number of columns │ 6      │ │ int32       │ 2     │                                                          │
│ └───────────────────┴────────┘ │ float64     │ 1     │                                                          │
│                                └─────────────┴───────┘                                                          │
│                                                     number                                                      │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┓  │
│ ┃ column_name             ┃ NA  ┃ NA %  ┃ mean    ┃ sd      ┃ p0     ┃ p25     ┃ p75     ┃ p100     ┃ hist   ┃  │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━┩  │
│ │ Year                    │   0 │     0 │    2000 │     3.7 │   2000 │    2000 │    2000 │     2000 │ ▅▅▅▅▅█ │  │
│ │ Population              │   0 │     0 │  560000 │  710000 │  14000 │  110000 │  620000 │  3400000 │  █▁▁▁  │  │
│ │ Year_population(mill    │   0 │     0 │      29 │     2.5 │     24 │      28 │      31 │       32 │ ▄▂▂▂██ │  │
│ └─────────────────────────┴─────┴───────┴─────────┴─────────┴────────┴─────────┴─────────┴──────────┴────────┘  │
│                                                     string                                                      │
│ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓  │
│ ┃ column_name               ┃ NA      ┃ NA %       ┃ words per row                ┃ total words              ┃  │
│ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━┩  │
│ │ Gender                    │       0 │          0 │                            1 │                      680 │  │
│ │ Nationality               │       0 │          0 │                            1 │                      680 │  │
│ │ Region                    │       0 │          0 │                            1 │                      680 │  │
│ └───────────────────────────┴─────────┴────────────┴──────────────────────────────┴──────────────────────────┘  │
╰────────────────────────────────────────────────────── End ──────────────────────────────────────────────────────╯

(2) Dataprep

!pip install dataprep

from dataprep.eda import create_report
create_report(df)

  0%|          | 0/884 [00:00<?, ?it/s]

c:\Users\Tariq Laptops\AppData\Local\Programs\Python\Python39\lib\site-packages\dask\core.py:119: RuntimeWarning:

invalid value encountered in divide

c:\Users\Tariq Laptops\AppData\Local\Programs\Python\Python39\lib\site-packages\dataprep\eda\distribution\render.py:274: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

DataPrep Report Overview

Variables ≡

Gender Nationality Region Year Population Year_population(million)

Interactions Correlations Missing Values

Overview

Dataset Statistics

Number of Variables	6
Number of Rows	676
Missing Cells	0
Missing Cells (%)	0.0%
Duplicate Rows	0
Duplicate Rows (%)	0.0%
Total Size in Memory	143.5 KB
Average Row Size in Memory	217.3 B
Variable Types	Categorical: 3 Numerical: 3

Dataset Insights

Population is skewed	Skewed
Year_population(million) is skewed	Skewed

Variables

Sort by Feature order Alphabetical Amount missing Approximate unique Reverse order

Gender

categorical

Show Details

Approximate Distinct Count	2
Approximate Unique (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory Size	47320

Stats Pie Chart Word Cloud Word Frequency Word Length

Length

Mean	5
Standard Deviation	1.0007
Median	5
Minimum	4
Maximum	6

Sample

1st row	Female
2nd row	Female
3rd row	Female
4th row	Female
5th row	Female

Letter

Count	3380
Lowercase Letter	2704
Space Separator	0
Uppercase Letter	676
Dash Punctuation	0
Decimal Number	0

• The top 2 categories (Female, Male) take over 50.0%

Nationality

categorical

Show Details

Approximate Distinct Count	2
Approximate Unique (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory Size	48672

Stats Pie Chart Word Cloud Word Frequency Word Length

Length

Mean	7
Standard Deviation	2.0015
Median	7
Minimum	5
Maximum	9

Sample

1st row	Non Saudi
2nd row	Non Saudi
3rd row	Non Saudi
4th row	Non Saudi
5th row	Non Saudi

Letter

Count	4394
Lowercase Letter	3380
Space Separator	338
Uppercase Letter	1014
Dash Punctuation	0
Decimal Number	0

• The top 2 categories (Non Saudi, Saudi) take over 50.0%

• The largest value (saudi) is over 2.0 times larger than the second largest value (non)

Region

categorical

Show Details

Approximate Distinct Count	13
Approximate Unique (%)	1.9%
Missing	0
Missing (%)	0.0%
Memory Size	50804

Stats Pie Chart Word Cloud Word Frequency Word Length

Length

Mean	10.1538
Standard Deviation	6.142
Median	8
Minimum	4
Maximum	24

Sample

1st row	Al Bahah
2nd row	Al Bahah
3rd row	Al Bahah
4th row	Al Bahah
5th row	Al Bahah

Letter

Count	6292
Lowercase Letter	5044
Space Separator	572
Uppercase Letter	1248
Dash Punctuation	0
Decimal Number	0

• The largest value (al) is over 6.0 times larger than the second largest value (ar)

Year

numerical

Show Details

Approximate Distinct Count	13
Approximate Unique (%)	1.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Memory Size	10816
Mean	2016
Minimum	2010
Maximum	2022
Zeros	0
Zeros (%)	0.0%
Negatives	0
Negatives (%)	0.0%

Stats KDE Plot Normal Q-Q Plot Box Plot

Quantile Statistics

Minimum	2010
5-th Percentile	2010
Q1	2013
Median	2016
Q3	2019
95-th Percentile	2022
Maximum	2022
Range	12
IQR	6

Descriptive Statistics

Mean	2016
Standard Deviation	3.7444
Variance	14.0207
Sum	1.3628e+06
Skewness	0
Kurtosis	-1.2143
Coefficient of Variation	0.001857

• Year is not normally distributed (p-value 0.003171129119882823)

Population

numerical

Show Details

Approximate Distinct Count	675
Approximate Unique (%)	99.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Memory Size	10816
Mean	558716.8994
Minimum	14304
Maximum	3406281
Zeros	0
Zeros (%)	0.0%
Negatives	0
Negatives (%)	0.0%

• Population is skewed right (γ1 = 1.888)

Stats KDE Plot Normal Q-Q Plot Box Plot

Quantile Statistics

Minimum	14304
5-th Percentile	28743
Q1	109897.5
Median	249559
Q3	618074.5
95-th Percentile	2.0754e+06
Maximum	3406281
Range	3391977
IQR	508177

Descriptive Statistics

Mean	558716.8994
Standard Deviation	706271.3702
Variance	4.9882e+11
Sum	3.7769e+08
Skewness	1.888
Kurtosis	2.9996
Coefficient of Variation	1.2641

• Population is not normally distributed (p-value 3.826579873826196e-12)

• Population has 95 outliers

Year_population(million)

numerical

Show Details

Approximate Distinct Count	12
Approximate Unique (%)	1.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Memory Size	10816
Mean	29.0692
Minimum	24
Maximum	32.2
Zeros	0
Zeros (%)	0.0%
Negatives	0
Negatives (%)	0.0%

• Year_population(million) is skewed left (γ1 = -0.7319)

Stats KDE Plot Normal Q-Q Plot Box Plot

Quantile Statistics

Minimum	24
5-th Percentile	24
Q1	27.6
Median	30.1
Q3	31
95-th Percentile	32.2
Maximum	32.2
Range	8.2
IQR	3.4

Descriptive Statistics

Mean	29.0692
Standard Deviation	2.5129
Variance	6.3145
Sum	19650.8
Skewness	-0.7319
Kurtosis	-0.7589
Coefficient of Variation	0.08644

• Year_population(million) is not normally distributed (p-value 8.354997363691557e-07)

Interactions

Correlations

Pearson Spearman KendallTau

Missing Values

Bar Chart Spectrum Heat Map Dendrogram

Report generated with DataPrep